2016/6/7 みんなのPython勉強会で発表した資料です。 scikit-learnの初心者向けに、データのまとめ方やドキュメントを読む時の心構えについて書いてあります。

1. Python
2016/6/7
Python

2. Python 3 3

3. Python
http://bit.ly/yoseiml

4. Python
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• scikit-learn
• Numpy/Scipy
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6. scikit-learn
model = SomeAlogrithm(hyperparameters)
model.fit(x,y)
prediction = model.predict(z)
model = SomeAlogrithm(hyperparameters)
model.fit(x)
prediction_x = model.labels_
prediction_z = model.predict(z)
model = SomeAlogrithm(hyperparameters)
model.fit(x)
transformed = model.transform(z)

7. scikit-learn
n×m n×1 n

8. from sklearn import datasets
from sklearn.svm import SVC
iris=datasets.load_iris()
data_train=iris.data[:-10,:]
target_train=iris.target[:-10]
data_eval=iris.data[-10:,:]
target_eval=iris.target[-10:]
svc=SVC()
svc.fit(data_train,target_train)
predicted=svc.predict(data_eval)
print("Accuracy: {}".format((target_eval==predicted).sum()/10.))

9. scikit-learn
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• scikit-learn
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11. …

12. 0 1 …
0 1 …
1 /1 Python
i j (i,j)

13. 0 1 2
3 4 5
6 7 8
9 10 11
a
1 [3,4,5]
0 [0,3,6,9]
(2,1) a[2,1]
1 a[1,:]
0 a[:,0]
(2,1) 7
>>> import numpy as np
>>> a=np.arange(12).reshape(4,3)
>>> a
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
>>> a[1,:]
array([3, 4, 5])
>>> a[2,1]
7
>>> a[:,0]
array([0, 3, 6, 9])
>>>

14. csv 9 10
import numpy as np
import csv
data = []
target = []
filename = "input_data.csv"
with open(filename) as f:
for row in csv.reader(f):
data.append([float(x) for x in row[:9]])
target.append(float(row[9]))
data = np.array(data)
target = np.array(target)

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• np.array

16. MovieLens
from scipy import sparse
items = []
users = []
ratings = []
for line in open("ml-100k/u.data"):
a = line.split("t")
users.append(int(a[0]))
items.append(int(a[1]))
ratings.append(int(a[2]))
n_users = max(users)
n_items = max(items)
mat = sparse.lil_matrix((n_users, n_items))
for u, i, r in zip(users, items, ratings):
mat[u - 1, i - 1] = r
mat = mat.tocsr()

17. • lil_matrix
• csr_matrix

18. scikit-learn

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20. scikit-learn
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• SVM SVC
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• SVM
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22. scikit-learn

23. np.meshgrid? np.c_? ravel??
???

24. …
model = SomeAlogrithm(hyperparameters)
model.fit(x,y)
prediction = model.predict(z)

25. • scikit-learn
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• scikit-learn
numpy matplotlib

26. …

27. …

28. Python
http://bit.ly/yoseiml

29. scikit-learn
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• OK